\section{Introduction}
Digital media, user-generated content and social networks enable effective human interaction; so much so that much of our day-to-day interaction is conducted online \cite{Viswanath:2009:EUI:1592665.1592675}. Interaction in social media fundamentally changes the way businesses and consumers behave \cite{Qualman-2012-socialnomics}, can be instrumental to the  success of individuals and  businesses \cite{Haenlein01092009}, and even affects the stability of political regimes \cite{Howard2011,Lamer2012}.
This fact forces organizations (businesses, governments, and non-profit organizations) to be constantly involved in the monitoring  of, and  the interaction  with, human agents in digital environments \cite{Langheinrich-2011}.
% Social-media presence is already a common practice  \cite{Haenlein01092009} and the analysis of content in social-media  is  becoming a necessity

Automatic analysis of user-generated online content benefits from extensive research and commercial opportunities.
% organizations  still face a challenge in automatically interacting with  audiences in such environments.
In natural language processing, there is ample research on the analysis of subjectivity and sentiment of content in social media. The development of tools for sentiment analysis \cite{Davidov:2010:ESL:1944566.1944594}, mood aggregation \cite{agichtein2008finding}, opinion mining \cite{Mishne06multipleranking}  and more now enjoys a wide interest and exposure, as is also evidenced by the many workshops and dedicated tracks  at ACL venues.\footnote{In particular,  the *ACL workshop series LASM \url{http://www.site.uottawa.ca/~diana/eacl2014-social-media-workshop.htm}
and WASSA \url{http://optima.jrc.it/wassa2014/}.}
%draw much interest within the ACL community as well as neighboring disciplines.}
%A different, however related, research topic, is the automatic identification of author characteristics, aiming to  classify authors' gender, geographic profile, interests, etc. (REFERENCE). %\footnote{E.g., there exists a dedicated workshop on entity attribution \url{www.cs.jhu.edu/~svitlana/workshop.html}.}
A related strand of research uses computational methods to find out  what kind of published utterances are influential, and how they affect linguistic communities \cite{Danescu-Niculescu-Mizil:2009:ORO:1526709.1526729}. Such work complements, and contributes to, studies from sociology  and sociolinguistics that aim to delineate the process of generating meaningful responses (e.g., \newcite{nla.cat-vn5407742}).

In contrast with these analysis efforts, the  topic of  {\em generating}  responses to content  in social media is only sparsely explored. Commercially, there is movement towards online response automation \cite{Owyang2012,Mah2012}.%
 %though there are arguments for \cite{Young2011} and against \cite{Shumchenia2011} such engagements.
 \footnote{There is a general debate on the efficiency of automated tools \cite{Nall2013} and whether such tools are even desirable in social media (\newcite{McConnell2012} and  user responses for \newcite{Owyang2012}).}
%though while there are tools that perform some of the interactive tasks automatically (
%
Research on user interfaces is trying to move away from script-based interaction towards the development of chat bots that attempt natural human-like interaction \cite{Mori:2003:ECF:604045.604096,Feng06anintelligent}. These chat bots are typically designed to provide an automated one-size-fits-all type of interaction. A recent study by  \newcite{Ritter:2011:DRG:2145432.2145500}  addresses the generation of responses to natural language tweets in a data-driven setup. It applies a machine-translation approach to response generation, where moods and sentiments already expressed in the past are reused or replicated. 

The present paper, in contrast,  addresses the problem of generating novel, {subjective} responses to online opinionated articles. We formally define the document-to-response mapping problem and suggest an end-to-end system to solve it.
%and therefore carries different characteristics. Moreover, this task is strongly related to grounded [replace: cogent?] natural language generation. Empirical evaluation of reactions to such generation is directly relevant to studies in artificial intelligence that aim to develop systems and metrics that emulate Turing-like tests \cite{Turing1950} [Add: which is an extension we add to \cite{Ritter:2011:DRG:2145432.2145500}].
%In this paper, we explore and evaluate the feasibility of
% automatically generating human-like and appropriate  responses to opinionated articles in social media. %We  formally define the underlying assumptions and necessary components of the  architecture, and integrate them in order to generate responses that are cogent [3rd time - is there another word we can use], relevant and concise. We   implement a solution that builds
 Our system integrates  a range of  NLP and NLG technologies (including topic  models,  sentiment analysis, and the integration of a knowledge graph) in order to design a flexible generation mechanism that allows us to vary the information in the input to the generation procedure. We  then use a Turing-inspired test to study the different factors that contribute to the perceived human-likeness and relevance of generated responses, and  show  %system affects the generated responses. As our main result shows,
 how the perception of responses depends on external knowledge and the expressed sentiment.

The remainder of this paper is organized as follows. The next section presents our proposal:  Section~\ref{sec:approach} describes the general approach, Section \ref{sec:formal} formalizes the proposal and Section~\ref{sec:impl} presents our  end-to-end architecture. This is then followed by our evaluation method and empirical results in Section~\ref{sec:eval}. We discuss related and  future work in Section~\ref{sec:future}, and   in Section~\ref{sec:conclude} we summarize and conclude.
